Resource scheduling method and system based on map data fusion

Abstract

The invention is suitable for the technical field of resource scheduling, and particularly relates to a resource scheduling method and system based on map data fusion, and the method comprises the steps: obtaining resource scheduling information; obtaining scheduling condition data of scheduling each resource to the demand point within the historical duration; multiple pieces of scheduling data are obtained; obtaining the share of the selected resource according to a change trend between the multiple pieces of scheduling data and the share of the selected resource; and scheduling the resources to the demand point. According to the resource scheduling method based on map data fusion provided by the invention, the problem that in a traditional resource scheduling process, when a demand point needs resources, a resource scheduling system blindly schedules resources corresponding to inaccurate shares to the demand point, so that in the process, resources mismatched with the demand point may be scheduled to the demand point, and the resource scheduling efficiency is improved can be solved. Therefore, the scheduling personnel can obtain the resources for mass prevention and mass treatment, such as medical care resources, unlocking resources, snake catching resources and the like.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of resource scheduling, and particularly relates to a resource scheduling method and system based on map data fusion. BACKGROUND

[0002] The resource scheduling method refers to a kind of technology and method for integrating and processing multiple source map data to optimize resource allocation and scheduling decision. It is widely used in the fields of traffic management, logistics distribution, emergency response, smart city, etc.

[0003] In the traditional resource scheduling process, when a demand point needs resources, the resource scheduling system will blindly schedule resources corresponding to inaccurate shares to the demand point, which may lead to the scheduling of resources that do not match the demand point to the demand point, resulting in the inability to reasonably schedule resources. SUMMARY

[0004] The embodiments of the application provide a resource scheduling method and system based on map data fusion, which can solve the problem that in the traditional resource scheduling process, when a demand point needs resources, the resource scheduling system will blindly schedule resources corresponding to inaccurate shares to the demand point, which may lead to the scheduling of resources that do not match the demand point to the demand point, resulting in the inability to reasonably schedule resources.

[0005] In a first aspect, the embodiments of the application provide a resource scheduling method based on map data fusion, comprising: obtaining resource scheduling information; wherein the resource scheduling information includes multiple resources, and the multiple resources are multiple resources with the highest priority determined based on map data fusion; the multiple resources are different from resources that have been successfully scheduled; obtaining scheduling condition data of each resource in different geographical regions to a demand point within a historical time period; when a scheduling instruction is detected in the current map, sequentially selecting resources of different shares, and sequentially obtaining multiple scheduling data according to the scheduling condition data corresponding to the multiple resources with the highest matching degree in the resource scheduling information and the scheduling condition data corresponding to all the resources; wherein the share of the multiple resources with the highest matching degree in the resource scheduling information is associated with the share of the selected resources; obtaining the share of the selected resources corresponding to the position with the largest change amplitude in the change trend according to the change trend between the multiple scheduling data and the share of the selected resources; scheduling the resources to a demand point according to the share of the selected resources corresponding to the position with the largest change amplitude in the change trend.

[0006] The technical solutions provided in the embodiments of the present application have at least the following technical effects. The resource scheduling method based on map data fusion provided in the present application, in the case that a scheduling instruction is detected in the current map, successively selects resources of different shares, and successively obtains multiple scheduling data according to the scheduling condition data corresponding to the multiple resources with the highest matching degree and the scheduling condition data corresponding to all the resources in the resource scheduling information. According to the change trend between the multiple scheduling data and the share of the selected resources, the share of the selected resources corresponding to the position with the largest change amplitude in the change trend is obtained. According to the share of the selected resources corresponding to the position with the largest change amplitude in the change trend, the resources are scheduled to the demand point. The share of the selected resources can be dynamically adjusted, blind scheduling of the resources corresponding to the inaccurate share to the demand point is avoided, the resources that do not match the demand point are scheduled to the demand point by the resource scheduling system in order to meet the requirement of the share, and the problem that the resources cannot be reasonably scheduled is solved, which is helpful for the resources to be reasonably scheduled.

[0007] In a possible implementation manner of the first aspect, after the resources are scheduled to the demand point according to the share of the selected resources corresponding to the position with the largest change amplitude in the change trend, the method further includes: In the case that the share of the selected resources corresponding to the position with the largest change amplitude in the change trend is inconsistent with the maximum share of the resources that can be carried in the current scheduling instruction, the resource configuration scale of the resource scheduling task is changed according to the share of the selected resources corresponding to the position with the largest change amplitude in the change trend, and the resource allocation scheme after the scale is adjusted is executed.

[0008] In a possible implementation manner of the first aspect, before the resources of different shares are successively selected in the case that a scheduling instruction is detected in the current map, and multiple scheduling data are successively obtained according to the scheduling condition data corresponding to the multiple resources with the highest matching degree and the scheduling condition data corresponding to all the resources in the resource scheduling information, the method further includes: When the trigger condition is detected, the resource scheduling system stops scheduling and starts a data fusion process; In the case that the resource scheduling system stops scheduling and starts the data fusion process, fusion feature information is recorded; wherein the fusion feature information is recorded in a target database, and the target database is a database in which the fusion feature information is associated with a fusion version number at the time when the resource scheduling system stops scheduling; When the resource scheduling system determines that the demand point matches the fusion feature information, and the difference between the current fusion version number and the fusion version number when the resource scheduling system stops scheduling is greater than a threshold, the resource scheduling system initiates the scheduling instruction to the demand point according to the fused map data; wherein when the difference between the current fusion version number and the fusion version number when the resource scheduling system completes data fusion is less than or equal to the threshold, the resource scheduling system receives the scheduling instruction generated by the demand point based on the preset rule.

[0009] In a possible implementation manner of the first aspect, the method further includes: In a case where the share of the selected resource corresponding to the position of the maximum change amplitude in the change trend exceeds the bearable resource share in the current scheduling task, the selected resource is scheduled in batches; In a case where the share of the selected resource corresponding to the position of the maximum change amplitude in the change trend does not exceed the bearable resource share in the current scheduling task, the resource configuration scale of the resource scheduling task is reduced.

[0010] In a possible implementation manner of the first aspect, the method further includes: In a case where there is a demand point with unallocated resources, the available resource information in the resource scheduling system that is not scheduled is obtained according to the first instruction; The scheduling situation data of each resource in the available resource information in different geographic regions is obtained, which is scheduled to the demand point in a historical time length; In a case where the scheduling instruction is detected in the current map, the step of sequentially selecting different shares of the resource, sequentially obtaining a plurality of scheduling data according to the scheduling situation data corresponding to a plurality of resources with the highest matching degree in the available resource information and the scheduling situation data corresponding to all the resources, obtaining the share of the selected resource corresponding to the position of the maximum change amplitude in the change trend according to the change trend between the plurality of scheduling data and the share of the selected resource, and scheduling the resource to the demand point according to the share of the selected resource corresponding to the position of the maximum change amplitude in the change trend is returned to be executed.

[0011] In a possible implementation manner of the first aspect, the method further includes: In a case where the share of the selected resource corresponding to the position of the maximum change amplitude in the change trend does not exceed the bearable resource share in the current scheduling task, the plurality of resources with the highest matching degree in the scheduling candidate information are scheduled to the demand point.

[0012] In a possible implementation manner of the first aspect, the method further includes: The resource scheduling system obtains the feature descriptor of the demand point; The resource scheduling system performs similarity calculation on the feature descriptor and the fusion feature information of the target database. In a case where the similarity calculation value is greater than or equal to a target value, the resource scheduling system determines that the demand point matches the fusion feature information.

[0013] In a possible implementation of the first aspect, the method further includes: Before the resource scheduling system initiates the scheduling instruction to the demand point according to the fused map data, the resource to be scheduled is in an unscheduled state.

[0014] In a possible implementation of the first aspect, the method further includes: In a case where the share of other resources being scheduled in the resource scheduling system is less than a first value, the resource scheduling system continues to generate the scheduling instruction.

[0015] In a second aspect, an embodiment of the present application provides a resource scheduling system for implementing the resource scheduling method based on map data fusion in any of the first aspect, the resource scheduling system being applied to a resource scheduling device, and the resource scheduling system including: An acquisition unit configured to acquire resource scheduling information, wherein the resource scheduling information includes a plurality of resources, the plurality of resources being a plurality of resources with the highest priority determined based on map data fusion, and the plurality of resources being different from resources that have been successfully scheduled at present; A detection unit configured to acquire scheduling condition data of each of the resources in different geographical regions being scheduled to demand points within a historical time length; A determination unit configured to, in a case where a scheduling instruction is detected in a current map, sequentially select different shares of the resources, and sequentially obtain a plurality of scheduling data according to the scheduling condition data of a plurality of resources with the highest matching degree in the resource scheduling information and the scheduling condition data of all the resources, wherein the share of the plurality of resources with the highest matching degree in the resource scheduling information is associated with the share of the selected resources; A calculation unit configured to obtain the share of the selected resources corresponding to a position with the largest change amplitude in a change trend between the plurality of scheduling data and the share of the selected resources according to the change trend; A scheduling unit configured to schedule the resources to a demand point according to the share of the selected resources corresponding to the position with the largest change amplitude in the change trend.

[0016] In a third aspect, an embodiment of the present application provides a resource scheduling device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for resource scheduling based on map data fusion according to any one of the first aspect when executing the computer program.

[0017] It can be understood that the beneficial effects of the second aspect to the third aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0019] Figure 1 is a flowchart of the method for resource scheduling based on map data fusion provided by an embodiment of the present application; Figure 2 is an implementation flowchart of initiating a scheduling instruction in the method for resource scheduling based on map data fusion provided by an embodiment of the present application; Figure 3 is an implementation flowchart of scheduling resources to demand points in the method for resource scheduling based on map data fusion provided by an embodiment of the present application; Figure 4 is a structural diagram of the resource scheduling system provided by an embodiment of the present application; Figure 5 is a structural diagram of the resource scheduling device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary details.

[0021] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] It should also be understood that the term “and / or” as used herein refers to any one of the associated listed items, optionally, additional items in some combinations, and all possible combinations of the associated listed items.

[0023] As used in the description of the application and the appended claims, the term “if’ can be interpreted to mean “when” or “upon” or “in response to determining” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if a described condition or event occurs” can be interpreted to mean “upon determining,” or “in response to determining” or “upon detecting,” or “in response to detecting” the described condition or event, depending on the context.

[0024] In addition, the terms “first,” “second,” “third,” etc. as used in the description of the application and the appended claims are used only to distinguish different steps or elements, and do not imply or suggest relative importance.

[0025] Reference in the specification to “one embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase “in one embodiment” or “in some embodiments” in various places in the specification are not necessarily all referring to the same embodiment, although it can. The terms “including,” “containing,” “having,” and variations thereof are meant to encompass the terms “including but not limited to.” The terms “coupled” and “connected” are used broadly and encompass both direct and indirect coupling, connections, and combinations thereof.

[0026] In the related art, the resource scheduling method refers to a kind of technology and method for optimizing allocation and scheduling decision of resources by using multi-source map data for integration processing. It is widely used in traffic management, logistics distribution, emergency response, smart city and other fields.

[0027] In the traditional resource scheduling process, when a demand point needs resources, the resource scheduling system will blindly schedule the resources corresponding to the inaccurate share to the demand point, which may lead to the scheduling of resources that do not match the demand point to the demand point, resulting in the resources being unable to be reasonably scheduled. The following is a specific description of the problem.

[0028] For example, when a demand point needs resources, the resource scheduling system blindly determines 3 resources (for example, a preset rule is to determine 3 resources), and selects 3 resources with the highest matching degrees from all resources (the 3 resources are different from the resources that have been successfully scheduled, and the total resources include: resource a with a matching degree of 0.8, resource b with a matching degree of 0.2, resource c with a matching degree of 0.15, and resource d with a matching degree of 0.1), and schedules the 3 resources (i.e., resource a with a matching degree of 0.8, resource b with a matching degree of 0.2, and resource c with a matching degree of 0.15) to the demand point. Although resource b and resource c have higher matching degrees than resource d in all resources and are in the second and third positions, resource b and resource c do not match the demand point, that is, the resource scheduling system forcibly schedules resources that do not match the demand point to the demand point in order to meet the processing logic of the preset rule of determining 3 resources, resulting in that the resources cannot be reasonably scheduled.

[0029] For example, when a demand point needs resources, the resource scheduling system blindly determines 2 resources (for example, a preset rule is to determine 2 resources), and selects 2 resources with the highest matching degrees from all resources (the 2 resources are different from the resources that have been successfully scheduled, and the total resources include: resource a with a matching degree of 0.8, resource b with a matching degree of 0.2, resource c with a matching degree of 0.15, and resource d with a matching degree of 0.1), and schedules the 2 resources (i.e., resource a with a matching degree of 0.8 and resource b with a matching degree of 0.2) to the demand point. Although resource a and resource b have higher matching degrees than resource c and resource d in all resources and are in the first and second positions, resource b does not match the demand point, that is, the resource scheduling system forcibly schedules resources that do not match the demand point to the demand point in order to meet the processing logic of the preset rule of determining 2 resources, resulting in that the resources cannot be reasonably scheduled.

[0030] To solve the above problems, the embodiments of the present application provide a resource scheduling method and system based on map data fusion.

[0031] In the method, in a case where a scheduling instruction is detected in the current map, different proportions of resources are sequentially selected, and a plurality of scheduling data is obtained according to scheduling condition data corresponding to a plurality of resources with the highest matching degree in the resource scheduling information and scheduling condition data corresponding to all resources. According to a change trend between the plurality of scheduling data and the proportion of the selected resources, a proportion of the selected resources corresponding to a position with the largest change amplitude in the change trend is obtained. According to the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend, the resources are scheduled to the demand point. The proportion of the selected resources can be dynamically adjusted, blind scheduling of the resources corresponding to the inaccurate proportion to the demand point is avoided, the resources that do not match the demand point are scheduled to the demand point by the resource scheduling system in order to meet the requirement of the proportion, and the problem that the resources cannot be reasonably scheduled is solved, which is helpful for the resources to be reasonably scheduled.

[0032] The resource scheduling method based on map data fusion provided by the embodiments of the present application can be applied to a resource scheduling device, and the resource scheduling device is the execution subject of the resource scheduling method based on map data fusion provided by the embodiments of the present application. The embodiments of the present application do not limit the specific type of the resource scheduling device.

[0033] For example, the resource scheduling device can be a mobile phone, a smart scheduling terminal (such as a dedicated scheduling console), a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a computing device, or a computer connected to a wireless modem, a laptop computer, a handheld communication device, a handheld computing device, and the like.

[0034] In order to better understand the resource scheduling method based on map data fusion provided by the embodiments of the present application, the specific implementation process of the resource scheduling method based on map data fusion provided by the embodiments of the present application is exemplarily introduced below.

[0035] Figure 1 A schematic flowchart of the resource scheduling method based on map data fusion provided by the embodiments of the present application is shown, and the resource scheduling method based on map data fusion includes: S100, resource scheduling information is obtained. The resource scheduling information includes a plurality of resources, and the plurality of resources are a plurality of resources with the highest priority determined based on map data fusion. The plurality of resources are different from resources that have been successfully scheduled.

[0036] It can be understood that the resource scheduling information is used to record the plurality of resources, and the resource scheduling information contains detailed information (such as resource ID, position coordinates, priority score, available time) of the plurality of resources. Each resource carries a resource type (such as a vehicle, a device, or a person) and a real-time geographic position of the resource.

[0037] For example, map data is obtained from multiple sources, such as real-time traffic data, geographic information system (GIS) data, satellite images, sensor data, etc. The real-time geographic location of each resource (such as a vehicle, a device, a person) is combined with each resource to obtain resource scheduling information. For each resource, features that affect resource scheduling priority are extracted, such as the spatial distance between the resource and the demand point (the shorter the spatial distance, the higher the priority), the traffic condition around the resource (such as congestion index, travel time, for example, the shorter the travel time, the higher the priority), the available state of the resource (for example, the higher the priority for the resource in the available state), etc. The resources with lower priority are filtered out, and the resources that have been successfully scheduled are filtered out. According to the filtered resources, a plurality of resources with the highest priority (such as 4, 5) are obtained.

[0038] In this way, by ranking the resource priority based on the map data fusion, the appropriate resource can be locked when selecting the resource, the scheduling error can be reduced, and the data basis for the subsequent reasonable scheduling of the resource can be provided.

[0039] S200, obtaining scheduling condition data of each resource scheduled to the demand point within a historical time length.

[0040] It can be understood that the scheduling condition data is used to reflect the scheduling condition of the resource, for example, the scheduling condition data can be the time data consumed from the start of scheduling to the completion of scheduling, can also be the actual driving or transmission distance data from the resource to the demand point, and can also be the frequency data of the resource being scheduled to the demand point. The historical time length can be 1 day, 2 days, 3 days, etc.

[0041] For example, within the historical time length, the time data consumed from the start of scheduling to the completion of scheduling of each resource can be obtained by a timer. Within the historical time length, the actual driving or transmission distance data from the resource to the demand point can also be obtained by map data. Within the historical time length, the frequency (number of scheduling times per day) of the resource being scheduled to the demand point can be obtained, for example, within 1 day (historical time length), the total number of scheduling tasks of resource a within the historical time length is divided by the total number of scheduling tasks of all resources within the historical time length.

[0042] In this way, the scheduling strategy of the resource can be improved, and the basis for subsequent scheduling can be provided.

[0043] S300, in the case that the scheduling instruction is detected in the current map, different proportions of resources are sequentially selected, and a plurality of scheduling data are obtained according to the scheduling condition data corresponding to the plurality of resources with the highest matching degree in the resource scheduling information and the scheduling condition data corresponding to all resources. The proportion of the plurality of resources with the highest matching degree in the resource scheduling information is associated with the proportion of the selected resources.

[0044] It can be understood that the scheduling instruction can be an instruction of scheduling resources to demand points. The matching degree is used to reflect the matching degree of the resources and the demand points. The scheduling data is used to reflect the scheduling efficiency of the resources.

[0045] For example, the different proportions of resources can be selected in sequence, such as 1, 2, 3, 4, 5, etc. 1 ≦ the different proportions of resources selected in sequence ≦ the proportion of the resources in the resource scheduling information. The proportions of the multiple resources with the highest matching degrees in the resource scheduling information are associated with the proportions of the resources selected. For example, if 1 proportion of the resources is selected, the scheduling condition data corresponding to the resource with the highest matching degree in the resource scheduling information is determined. If 1 proportion of the resources is selected, the scheduling condition data corresponding to the two resources with the highest matching degrees in the resource scheduling information is determined. That is, the scheduling condition data corresponding to the several resources with the highest matching degrees in the resource scheduling information is determined when several proportions of the resources are selected.

[0046] For example, the matching degree can be calculated according to the time data spent by each resource from the start of scheduling to the completion of scheduling and the actual driving or transmission distance data of the resource from the departure to the demand point. For example, the matching degree = w1 × Sd + w2 × St, where Sd is the distance, St is the time data, w1 is the first coefficient (such as 0.4), and w2 is the first coefficient (such as 0.6).

[0047] For example, the scheduling data under the selection of different proportions of resources is calculated in sequence. For example, when 1 proportion is selected, the scheduling data is obtained according to the scheduling condition data corresponding to the resource with the highest matching degree in the resource scheduling information and the scheduling condition data corresponding to all the resources, such as the scheduling condition data corresponding to the resource with the highest matching degree is 8 scheduling times / day, and the scheduling condition data corresponding to all the resources is 60 scheduling times / day, so the scheduling data is 8 ÷ 60 = 0.13. For another example, when 2 proportions are selected, the scheduling data is obtained according to the scheduling condition data corresponding to the two resources with the highest matching degrees in the resource scheduling information and the scheduling condition data corresponding to all the resources, such as the scheduling condition data corresponding to the two resources with the highest matching degrees is 8 scheduling times / day and 10 scheduling times / day, and the scheduling condition data corresponding to all the resources is 60 scheduling times / day, so the scheduling data is 0.13 + 10 ÷ 60 = 0.3. For another example, when 3 proportions are selected, the scheduling data is obtained according to the scheduling condition data corresponding to the three resources with the highest matching degrees in the resource scheduling information and the scheduling condition data corresponding to all the resources, such as the scheduling condition data corresponding to the three resources with the highest matching degrees is 8 scheduling times / day, 10 scheduling times / day and 30 scheduling times / day, and the scheduling condition data corresponding to all the resources is 60 scheduling times / day, so the scheduling data is (8 + 10 + 30) ÷ 60 = 0.8. In this way, multiple scheduling data can be obtained.

[0048] In this way, the scheduling data corresponding to each share resource can be calculated, and the change trend of the multiple scheduling data helps to subsequently find the best balance point between the resource share and the scheduling efficiency, and then intelligently adjust the share of resource scheduling, avoid blindly scheduling the resource corresponding to the inaccurate share to the demand point, and make the resource scheduling system schedule the resource that does not match the demand point to the demand point in order to meet the requirement of the share, which leads to the problem that the resource cannot be reasonably scheduled. It is helpful to reasonably schedule the resource.

[0049] S400, obtaining the share of the selected resource corresponding to the position with the maximum change amplitude in the change trend according to the change trend between the multiple scheduling data and the share of the selected resource.

[0050] It can be understood that the change amplitude is used to reflect the degree of improvement or decline of scheduling performance per additional resource.

[0051] In the traditional technology, the resource share corresponding to the maximum scheduling data is selected as the optimal selected resource share, but this process has a problem, for example, the effect of increasing a certain resource on improving the scheduling performance is very small, for example, when 2 shares are selected, the scheduling data is 0.3, when 3 shares are selected, the scheduling data is 0.35, although the scheduling data is larger, the resource input and the performance improvement are not proportional, that is, increasing the resource share may bring very small performance improvement, that is, in order to blindly pursue the highest value of the scheduling data, the resource corresponding to the inaccurate share is scheduled to the demand point, which may schedule the resource that does not match the demand point to the demand point, leading to the problem that the resource cannot be reasonably scheduled.

[0052] For example, taking the resource share as the independent variable (for example, 1 share, 2 shares, 3 shares, etc.) and the corresponding scheduling data as the dependent variable, a set of sequence data {(f1, d1), (f2, d2), …, (fx, dx)} is obtained, where fx is the selected resource share, and dx is the corresponding scheduling data. The change amplitude (difference or change rate) of adjacent scheduling data is calculated, △x = |dx+1-dx|, and the change amplitude reflects the degree of improvement or decline of scheduling performance per additional resource. The position with the maximum change amplitude is found in the difference sequence, and the corresponding resource share is taken as the optimal selected resource share. That is, the share of the selected resource is selected according to the change trend between the multiple scheduling data and the share of the selected resource, rather than simply relying on the value of the scheduling data itself, avoiding blindly pursuing the highest value of the scheduling data, leading to scheduling the resource corresponding to the inaccurate share to the demand point, and then scheduling the resource that does not match the demand point to the demand point, leading to the problem that the resource cannot be reasonably scheduled.

[0053] In this way, by analyzing the change range of the adjacent resource shares, the position of the resource share with the largest performance improvement can be accurately found, so that the case of too much resource investment but small performance improvement can be avoided, and the problem that the resource scheduling system blindly schedules the resource corresponding to the inaccurate share to the demand point, and the resource that does not match the demand point is scheduled to the demand point, so that the resource cannot be reasonably scheduled, can be avoided.

[0054] In step S500, the resource is scheduled to the demand point according to the selected resource share corresponding to the position with the largest change range in the change trend.

[0055] It can be understood that the multiple resources scheduled to the demand point are the multiple resources with the highest matching degree in the resource scheduling information, and the share of the multiple resources scheduled to the demand point is the selected resource share corresponding to the position with the largest change range in the change trend.

[0056] For example, according to the specific position of the demand point, a scheduling instruction is initiated to the selected resource. The scheduling instruction is sent to the terminal corresponding to the selected resource through the communication interface of the resource scheduling system, and the terminal corresponding to the resource receives the scheduling instruction, and moves to the demand point according to the fused map data. When the resource reaches the demand point and completes the scheduled task, the resource scheduling system updates the resource state to "scheduled completion", and records the scheduling task execution result.

[0057] In summary, in the scheduling process of the present application, on the one hand, the problem that the resource scheduling system blindly determines the resource corresponding to the inaccurate share, and schedules the resource that does not match the demand point to the demand point, so that the resource cannot be reasonably scheduled, will not occur because of the preset rule (for example, the preset rule is to determine 3 shares of resources). On the other hand, the problem that the resource scheduling system blindly pursues the highest value of the scheduling data, and schedules the resource corresponding to the inaccurate share to the demand point, and then schedules the resource that does not match the demand point to the demand point, so that the resource cannot be reasonably scheduled, will not occur.

[0058] In this way, the share of the selected resource can be dynamically adjusted, the problem that the resource scheduling system blindly schedules the resource corresponding to the inaccurate share to the demand point, and schedules the resource that does not match the demand point to the demand point, so that the resource cannot be reasonably scheduled, can be avoided, and the resource can be reasonably scheduled.

[0059] In a possible implementation, after step S500, the resource is scheduled to the demand point according to the selected resource share corresponding to the position with the largest change range in the change trend, the resource scheduling method based on map data fusion further includes: In the case that the share of the selected resource corresponding to the position with the largest change amplitude in the change trend is inconsistent with the share of the largest resource that can be carried in the current scheduling instruction, the resource configuration scale of the resource scheduling task is changed according to the share of the selected resource corresponding to the position with the largest change amplitude in the change trend, and the resource allocation scheme after the scale is adjusted is executed.

[0060] It can be understood that, in the case that the share of the selected resource corresponding to the position with the largest change amplitude in the change trend is greater than the share of the largest resource that can be carried in the current scheduling instruction, it is indicated that the optimal share exceeds the largest resource share that can be carried at present, and the share of the selected resource corresponding to the position with the largest change amplitude in the change trend is divided into multiple batches for scheduling, and the resource scheduling amount of each batch does not exceed the maximum carrying capacity. In the case that the share of the selected resource corresponding to the position with the largest change amplitude in the change trend is less than the share of the largest resource that can be carried in the current scheduling instruction, the resource configuration scale of the resource scheduling task is reduced, so as to avoid the problem that the resource that does not match the demand point is scheduled to the demand point, resulting in that the resource cannot be reasonably scheduled.

[0061] In this way, complex and changeable scheduling tasks can be flexibly coped with, and the problem that the resource that does not match the demand point is scheduled to the demand point, resulting in that the resource cannot be reasonably scheduled, can be effectively avoided.

[0062] In a possible implementation manner, in step S300, in the case that the scheduling instruction is detected in the current map, before the multiple scheduling data are obtained according to the scheduling condition data corresponding to the multiple resources with the highest matching degrees and the scheduling condition data corresponding to all resources in the resource scheduling information in turn according to the resources with different shares in turn selected, the resource scheduling method based on map data fusion further includes the following steps. S310, when the trigger condition is detected, the resource scheduling system stops scheduling and starts a data fusion process.

[0063] It can be understood that the trigger condition is used to instruct the resource scheduling system to stop scheduling and start the data fusion process.

[0064] For example, in the resource scheduling system, a monitoring module is designed to detect the trigger condition in real time. The monitoring module will continuously monitor the system, and when the trigger condition is detected, the system is notified to enter the next process through a signal or an event.

[0065] For example, once the scheduling stops, the system should start the data fusion process and begin to integrate information from different data sources. For example, the resource usage, task execution status and other information of the system are collected from multiple data sources. Different data sources are fused into a unified feature set by using a suitable fusion method (such as weighted average, PCA, machine learning algorithm, etc.).

[0066] In this way, the real-time monitoring trigger condition helps the system to timely perceive the environmental changes or abnormal conditions.

[0067] S320, in the case that the resource scheduling system stops scheduling and starts the data fusion process, record the fusion feature information. Wherein, the fusion feature information is recorded in the target database, and the target database is a database associated with the fusion version number when the resource scheduling system stops scheduling.

[0068] It can be understood that the fusion feature information is used to reflect the fused multi-source map data or key features. For example, the fusion feature information can include the map data features generated after fusion, the environmental state description, and the summary information of the resource state after fusion, etc. For example, the fusion feature information can be a feature set recording "main road congestion + 3 kilometers away from medical resources", and the fusion feature information can also be a feature set recording "telephone number + address + 1 kilometer away from unlocking resources". The fusion version number N can be a "data fusion version identifier" recorded at time V (for example, V corresponds to the 5th fusion, N = 5), which is used to mark the "new and old degree" of this fusion.

[0069] For example, a unique fusion version number is generated in combination with the fusion timestamp, data snapshot hash value or fusion algorithm running state. The fusion version number is used to identify the version of the current fusion feature information. The fusion feature information and the fusion version number are associated and encapsulated to form a structured data record, which at least contains: the fusion version number, the fusion feature information content (such as the fused map metadata, resource state summary, etc.), and the above record is written into the target database. The target database is designed as a storage structure associated with the fusion feature information and the fusion version number, which supports query and management.

[0070] In this way, the association storage of the version number and the fusion feature information effectively avoids data confusion and version conflict, and provides a data basis for subsequent decision-making.

[0071] S330, when the resource scheduling system determines that the demand point matches the fusion feature information, and the difference between the current fusion version number and the fusion version number when the resource scheduling system stops scheduling is greater than a threshold value, the resource scheduling system initiates a scheduling instruction to the demand point according to the fused map data. Wherein, when the difference between the current fusion version number and the fusion version number when the resource scheduling system completes data fusion is less than or equal to the threshold value, the resource scheduling system receives the scheduling instruction generated by the demand point based on the preset rule.

[0072] It can be understood that the preset rule can be that the resource is scheduled to the demand point at a fixed time every day, or the resource is scheduled to the demand point after a certain period of time.

[0073] In the traditional technical solution, some resources are set to be dispatched to the demand point at a fixed time every day or after a certain interval. However, there is a problem. When the resource is just dispatched to the demand point, the resource scheduling system starts the data fusion process and updates a lot of information (for example, the key information such as road construction and commercial area change has been updated). When waiting for the next demand point to need the resource, the old fusion data is still used as the basis (the demand point may continue to use the resource next time because the resource was just used last time, and the demand point does not know that the resource scheduling system starts the data fusion process in the time period from the last time to the next time), which may no longer be applicable to the resource.

[0074] For example, when the resource scheduling system determines that the demand point matches the fusion feature information, and the difference between the current fusion version number and the fusion version number when the resource scheduling system stops dispatching is greater than the threshold value, it indicates that the fusion data has changed significantly, and the old fusion data based on which the current demand point is generated is no longer applicable. The resource scheduling system recalculates the matching degree and path planning of the resource and the demand point based on the latest fused map data, and automatically generates and initiates a new dispatch instruction to the demand point, so that the dispatch instruction is generated based on the latest and high-quality fusion information.

[0075] For example, when the difference between the current fusion version number and the fusion version number when the resource scheduling system completes data fusion is less than or equal to the threshold value, it indicates that the fusion data changes little (only some data may be fine-tuned, and the core information does not change), and the dispatch instruction generated by the current demand point based on the preset rule is still effective, that is, the system does not actively initiate a dispatch, but waits for the demand point to dispatch the resource used last time according to the preset rule. That is, it can dynamically determine when the system actively initiates a new dispatch instruction to the demand point and when it does not actively initiate a new dispatch instruction to the demand point, thereby avoiding frequent initiation of a new dispatch instruction to the demand point and causing waste of resources.

[0076] In this way, by dynamically determining whether it is necessary to recalculate the matching degree of the resource and the demand point based on the latest fusion data, the system scheduling decision is always based on the most accurate and latest information. If the current fusion data has changed significantly, the resource scheduling system can respond in time, recalculate and initiate a new dispatch instruction, thereby avoiding making decisions based on outdated or inapplicable data to maximize the matching degree of the dispatched resource and the demand point. At the same time, frequent initiation of a new dispatch instruction to the demand point can be avoided. When the fusion data changes little and the core information does not change, the system does not actively initiate a new dispatch instruction, but waits for the demand point to continue to use the resource used last time based on the preset rule, which can reduce the over-intervention of the system, avoid unnecessary resource scheduling, reduce the burden of the scheduling system, and reduce resource consumption and waste.

[0077] In a possible implementation, the resource scheduling method based on map data fusion further includes: S340, in the case that the share of the selected resource corresponding to the position with the maximum change amplitude in the change trend exceeds the share of the bearable resource in the current scheduling task, the selected resource is scheduled in batches.

[0078] It can be understood that, in the case that the share of the selected resource corresponding to the position with the maximum change amplitude in the change trend is greater than the share of the maximum bearable resource in the current scheduling instruction, it is indicated that the optimal share exceeds the maximum bearable resource share, and the share of the selected resource corresponding to the position with the maximum change amplitude in the change trend is divided into multiple batches for scheduling, and the resource scheduling amount of each batch does not exceed the maximum bearing capacity.

[0079] In this way, complex and changeable scheduling tasks can be flexibly coped with, and the problem that resources that do not match demand points are scheduled to the demand points, resulting in that the resources cannot be reasonably scheduled, can be effectively avoided.

[0080] S350, in the case that the share of the selected resource corresponding to the position with the maximum change amplitude in the change trend does not exceed the share of the bearable resource in the current scheduling task, the resource configuration scale of the resource scheduling task is reduced.

[0081] It can be understood that, in the case that the share of the selected resource corresponding to the position with the maximum change amplitude in the change trend is less than the share of the maximum bearable resource in the current scheduling instruction, the resource configuration scale of the resource scheduling task is reduced, and the problem that resources that do not match demand points are scheduled to the demand points, resulting in that the resources cannot be reasonably scheduled, can be avoided.

[0082] In this way, complex and changeable scheduling tasks can be flexibly coped with, and the problem that resources that do not match demand points are scheduled to the demand points, resulting in that the resources cannot be reasonably scheduled, can be effectively avoided.

[0083] In a possible implementation, the resource scheduling method based on map data fusion further includes: S360, in the case that there is a demand point to which no resource is allocated, available resource information in the resource scheduling system that is not scheduled is obtained according to the first instruction.

[0084] It can be understood that the available resource information is used to record a plurality of resources that have not been scheduled, and the available resource information includes detailed information (such as resource ID, position coordinates, priority score, available time) of the plurality of resources. Each resource carries a resource type (such as a vehicle, a device, or a person) and a real-time geographic position of the resource. The first instruction is used to obtain the available resource information that is not scheduled.

[0085] For example, map data is obtained from multiple sources, such as real-time traffic data, geographic information system (GIS) data, satellite images, sensor data, etc. The real-time geographic location of each resource (such as a vehicle, a device, a person) is combined with each resource to obtain available resource information. For each resource, features that affect resource scheduling priority are extracted, such as the spatial distance between the resource and the demand point (the shorter the spatial distance, the higher the priority), the traffic condition around the resource (such as congestion index, travel time, for example, the shorter the travel time, the higher the priority), the availability of the resource (for example, the higher the priority for the resource in the available state), etc. The resources with lower priority are filtered out, and the resources that have been successfully scheduled are filtered out. According to the filtered resources, a plurality of resources with the highest priority (such as 4, 5) are obtained.

[0086] For example, in the case where there is a demand point with unallocated resources, according to the first instruction, the available resource information in the resource scheduling system is obtained.

[0087] In this way, the state of all unscheduled resources can be grasped, providing data support for subsequent decision-making.

[0088] S370, obtain scheduling condition data of each resource in the available resource information in the historical time period.

[0089] It can be understood that the scheduling condition data is used to reflect the scheduling condition of the resource, for example, the scheduling condition data can be the time data spent by the resource from the start of the scheduling to the completion of the scheduling, or the actual driving or transmission distance data of the resource from the departure to the demand point, or the frequency data of the resource being scheduled to the demand point. The historical time period can be 1 day, 2 days, 3 days, etc.

[0090] In this way, the scheduling strategy of the resource can be improved, providing a basis for subsequent scheduling.

[0091] S380, in the case where the scheduling instruction is detected in the current map, return to execute the step of sequentially selecting resources of different shares, sequentially obtaining a plurality of scheduling data according to the scheduling condition data corresponding to the plurality of resources with the highest matching degree in the available resource information and the scheduling condition data corresponding to all resources, obtaining the share of the selected resource corresponding to the position with the largest change amplitude in the change trend between the plurality of scheduling data and the share of the selected resource, and scheduling the resource to the demand point according to the share of the selected resource corresponding to the position with the largest change amplitude in the change trend.

[0092] It can be understood that, in the case that the scheduling instruction is detected in the current map, it is explained that the demand point has not been allocated any resource, and the demand point does not need the resource, but the resource has not been scheduled, and therefore, the steps S300, S400 and S500 can be returned to schedule the resource corresponding to the accurate share to the demand point, so as to avoid the problem that the resource scheduling system schedules the resource that does not match the demand point to the demand point in order to meet the requirement of the share, and the resource cannot be reasonably scheduled, and the resource can be reasonably scheduled.

[0093] In this way, the share of the selected resource can be dynamically adjusted, the resource corresponding to the inaccurate share is blindly scheduled to the demand point is avoided, the problem that the resource scheduling system schedules the resource that does not match the demand point to the demand point in order to meet the requirement of the share, and the resource cannot be reasonably scheduled is avoided, and the resource can be reasonably scheduled.

[0094] In a possible implementation, the resource scheduling method based on map data fusion further includes: In a case that the share of the selected resource corresponding to the position with the largest change amplitude in the change trend does not exceed the bearable resource share in the current scheduling task, the multiple resources with the highest matching degree in the candidate information are scheduled to the demand point.

[0095] It can be understood that the candidate information is used to record multiple backup resources. That is, the one-to-one correspondence between the resources recorded in the candidate information and the resources recorded in the resource scheduling information, and the resources recorded in the candidate information are backup resources of the resources recorded in the resource scheduling information. That is, the resources recorded in the candidate information have not been formally scheduled, but have the qualification to participate in the scheduling.

[0096] For example, in a case that the share of the selected resource corresponding to the position with the largest change amplitude in the change trend does not exceed the bearable resource share in the current scheduling task, the multiple resources with the highest matching degree in the candidate information can be selected, and the multiple resources with the highest matching degree in the candidate information are scheduled to the demand point.

[0097] In this way, the multiple backup resources recorded in the candidate information can be fully utilized, and the resource can be reasonably scheduled.

[0098] In a possible implementation, the resource scheduling method based on map data fusion further includes: S500, the resource scheduling system acquires the feature descriptor of the demand point.

[0099] It can be understood that the feature descriptor of the demand point can be a semantic feature, for example, a POI type (hospital / shopping mall / school), an administrative region code, etc. It can also be a geographic position.

[0100] In this way, the business attributes and environmental background of the demand point can be understood, and a data basis can be provided for subsequent processing.

[0101] In S600, the resource scheduling system performs similarity calculation on the feature descriptor and the fusion feature information of the target database.

[0102] It can be understood that the geographical position (latitude and longitude, etc.) is converted into a spatial coordinate vector, the cosine value of the included angle of the two vectors is calculated to measure the direction similarity. Or calculate the absolute distance in the vector space, take the reciprocal to convert into similarity. Or, the similarity of the text features is calculated.

[0103] In this way, by converting the geographical position into a spatial coordinate vector, the system can quantify the similarity of the spatial position by using the cosine similarity or distance reciprocal method, and accurately reflect the spatial correlation between the demand point and the fusion data.

[0104] In S700, in the case where the similarity calculation value is greater than or equal to the target value, the resource scheduling system determines that the demand point matches the fusion feature information.

[0105] It can be understood that the system pre-sets a similarity threshold (target value), which is a standard limit for judging whether the demand point feature descriptor matches the fusion feature information. The calculated similarity value is compared with the target value, and when the condition is met, the system confirms that the environmental features corresponding to the demand point are highly consistent with the fusion data; otherwise, it is determined that the demand point does not match the fusion feature information.

[0106] In this way, by setting a reasonable similarity threshold, the system can effectively distinguish between matching and non-matching demand points and fusion feature information, avoid resource mismatch or scheduling failure due to misjudgment, and make the environmental features of the demand point consistent with the fused data to trigger subsequent scheduling decisions based on the fusion information.

[0107] In one possible implementation, the resource scheduling method based on map data fusion further includes: Before the resource scheduling system initiates a scheduling instruction to the demand point according to the fused map data, the to-be-scheduled resource is in an unscheduled state.

[0108] It can be understood that the system continuously monitors the state information of all resources to confirm whether the target resource is in an "idle", "unscheduled" or "available" state. Before initiating the scheduling instruction, the system judges the to-be-scheduled resource, and only when it is confirmed that the resource is in an "unscheduled" state and is not occupied by other tasks, the scheduling instruction is allowed to be initiated.

[0109] In this way, by continuously monitoring and confirming that the resource is in an "unscheduled" or "idle" state, it is prevented that the same resource is allocated by multiple scheduling tasks at the same time, and resource conflict and repeated scheduling are avoided.

[0110] In a possible implementation, the resource scheduling method based on map data fusion further includes: If the share of other resources being scheduled in the resource scheduling system is less than the first value, the resource scheduling system continues to generate the scheduling instruction.

[0111] It can be understood that the first value is used to reflect the computing capability of the resource scheduling system.

[0112] For example, if the share of other resources being scheduled in the resource scheduling system is less than the first value, it indicates that the computing capability of the resource scheduling system is sufficient, and the performance will not be reduced or the task will not be delayed due to the computing resource bottleneck or excessive scheduling pressure, and the steps S300, S400 and S500 can be continued.

[0113] In this way, the scheduling rhythm can be dynamically adjusted, the resource utilization and the computing capability are balanced, and the efficient and stable operation of the scheduling process is facilitated.

[0114] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0115] Corresponding to the resource scheduling method based on map data fusion described in the above embodiments, the embodiments of the present application also provide a resource scheduling system, and each unit of the system can implement each step of the resource scheduling method based on map data fusion. Figure 4 The structure block diagram of the resource scheduling system provided by the embodiments of the present application is shown, and only the parts related to the embodiments of the present application are shown for ease of illustration.

[0116] Referring to Figure 4 , the resource scheduling system includes: An acquisition unit is configured to acquire resource scheduling information. The resource scheduling information includes a plurality of resources, and the plurality of resources are a plurality of resources with the highest priority determined based on map data fusion. The plurality of resources are different from resources that have been successfully scheduled at present.

[0117] A detection unit is configured to acquire scheduling condition data of each resource being scheduled to a demand point within a historical time length.

[0118] A determination unit is configured to, in a case where a scheduling instruction is detected in a current map, sequentially select resources with different shares, and sequentially obtain a plurality of scheduling data according to the scheduling condition data corresponding to the plurality of resources with the highest matching degree in the resource scheduling information and the scheduling condition data corresponding to all resources. The share of the plurality of resources with the highest matching degree in the resource scheduling information is associated with the share of the selected resources.

[0119] The computing unit is configured to obtain the share of the selected resource corresponding to the position of the maximum change range in the change trend according to the change trend and the share of the selected resource.

[0120] The scheduling unit is configured to schedule the resource to the demand point according to the share of the selected resource corresponding to the position of the maximum change range in the change trend.

[0121] It should be noted that the information interaction, execution process and the like between the above systems / units are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by the same can be referred to the method embodiments part, which will not be described here.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units is taken as an example for illustration, and in actual application, the above functions can be completed by different functional units according to needs, that is, the internal structure of the system is divided into different functional units to complete all or part of the above described functions. Each functional unit in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific name of each functional unit is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the unit in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0123] The present application also provides a resource scheduling device, Figure 5 The structure of the resource scheduling device provided by an embodiment of the present application is shown in the figure. Figure 5 As shown in the figure, the resource scheduling device 6 of this embodiment includes at least one processor 60 (only one is shown in the figure), at least one memory 61 (only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the resource scheduling device 6 realizes the steps in any of the above resource scheduling method embodiments based on map data fusion, or the resource scheduling device 6 realizes the functions of the units in the above system embodiments. Figure 5 Figure 5

[0124] ​​Exemplarily, the computer program 62 can be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 62 in the resource scheduling device 6.

[0125] The resource scheduling device 6 can be a mobile phone, a smart scheduling terminal (such as a dedicated dispatch console), a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a computing device, or a computer connected to a wireless modem, a laptop computer, a handheld communication device, a handheld computing device, and the like. The resource scheduling device 6 can include, but is not limited to, the processor 60 and the memory 61. Those skilled in the art can understand that the resource scheduling device 6 can include more or fewer components, or combine certain components, or different components, for example, it can also include an input / output device, a network access device, a bus, and the like. Figure 5 The resource scheduling device 6 is only an example and does not constitute a limitation on the resource scheduling device 6, and can include more or fewer components than the diagram, or combine certain components, or different components, for example, it can also include an input / output device, a network access device, a bus, and the like.

[0126] The processor 60 can be a central processing unit (CPU), and the processor 60 can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0127] The memory 61 can be an internal storage unit of the resource scheduling device 6 in some embodiments, for example, a hard disk or a memory of the resource scheduling device 6. The memory 61 can also be an external storage device of the resource scheduling device 6 in other embodiments, for example, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the resource scheduling device 6. Further, the memory 61 can include both the internal storage unit and the external storage device of the resource scheduling device 6. The memory 61 is used to store operating systems, application programs, BootLoader, data, and other programs, for example, program codes of the computer programs, etc. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0128] The computer readable storage medium stores the computer program, and the computer program is executed by the processor to implement the steps in any of the above method embodiments.

[0129] The computer program product, when running on the resource scheduling device, enables the resource scheduling device to implement the steps in any of the above method embodiments.

[0130] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the processes in the above embodiments, which can be completed by instructing related hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program, when executed by a processor, can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the resource scheduling device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc.

[0131] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0132] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0133] In the embodiments provided in the present application, it should be understood that the disclosed resource scheduling device, resource scheduling system and resource scheduling method based on map data fusion can be implemented in other ways. For example, the above-described resource scheduling device / resource scheduling system embodiments are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, another division manner can be used. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0134] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0135] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A resource scheduling method based on map data fusion, characterized in that, The method comprises: obtaining resource scheduling information; wherein the resource scheduling information comprises a plurality of resources, and the plurality of resources are a plurality of resources with the highest priority determined based on map data fusion; the plurality of resources are different from resources that have been successfully scheduled at present; obtaining scheduling situation data of each resource in a historical time period; in the case that a scheduling instruction is detected in the current map, sequentially selecting different proportions of the resources, and sequentially obtaining a plurality of scheduling data according to the scheduling situation data corresponding to the plurality of resources with the highest matching degree in the resource scheduling information and the scheduling situation data corresponding to all the resources; wherein the proportion of the plurality of resources with the highest matching degree in the resource scheduling information is associated with the proportion of the selected resources; obtaining the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend between the plurality of scheduling data and the proportion of the selected resources; scheduling the resources to demand points according to the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend.

2. The resource scheduling method based on map data fusion according to claim 1, wherein, After scheduling the resources to demand points according to the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend, the method further comprises: in the case that the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend is inconsistent with the proportion of the maximum resources that can be carried in the current scheduling instruction, changing the resource configuration scale of the resource scheduling task according to the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend, and executing the resource allocation scheme after the scale is adjusted.

3. The resource scheduling method based on map data fusion according to claim 1, wherein, Before sequentially selecting different proportions of the resources and sequentially obtaining a plurality of scheduling data according to the scheduling situation data corresponding to the plurality of resources with the highest matching degree in the resource scheduling information and the scheduling situation data corresponding to all the resources in the case that a scheduling instruction is detected in the current map, the method further comprises: when a trigger condition is detected, the resource scheduling system stops scheduling and starts a data fusion process; in the case that the resource scheduling system stops scheduling and starts the data fusion process, recording fusion feature information; wherein the fusion feature information is recorded in a target database, and the target database is a database in which the fusion feature information is associated with a fusion version number when the resource scheduling system stops scheduling; when the resource scheduling system determines that a demand point matches the fusion feature information and the difference between the current fusion version number and the fusion version number when the resource scheduling system stops scheduling is greater than a threshold value, the resource scheduling system initiates the scheduling instruction to the demand point according to the fused map data; wherein when the difference between the current fusion version number and the fusion version number when the resource scheduling system completes data fusion is less than or equal to the threshold value, the resource scheduling system receives the scheduling instruction generated by the demand point based on a preset rule.

4. The resource scheduling method based on map data fusion according to claim 2, wherein, The method further comprises: in the case that the proportion of the selected resources corresponding to the position with the largest change amplitude in the change trend exceeds the resource proportion that can be carried in the current scheduling task, the selected resources are scheduled in batches; In a case where the share of the resource corresponding to the position of the maximum change amplitude in the change trend does not exceed the share of the resource that can be carried in the current scheduling task, the resource configuration scale of the resource scheduling task is reduced.

5. The method for resource scheduling based on map data fusion according to claim 1, wherein, The method further comprises: In a case where there is a demand point with unallocated resources, obtaining, according to a first instruction, available resource information that is not scheduled in the resource scheduling system; Obtaining scheduling condition data of each of the resources in different geographic regions in a historical time period; In a case where a scheduling instruction is detected in the current map, returning to the step of sequentially selecting different shares of the resources, sequentially obtaining a plurality of scheduling data according to the scheduling condition data corresponding to the plurality of resources with the highest matching degree in the available resource information and the scheduling condition data corresponding to all the resources, and obtaining the share of the resource corresponding to the position of the maximum change amplitude in the change trend between the plurality of scheduling data and the share of the selected resource, and scheduling the resources to the demand point according to the share of the resource corresponding to the position of the maximum change amplitude in the change trend.

6. The method for resource scheduling based on map data fusion according to claim 1, wherein, The method further comprises: In a case where the share of the resource corresponding to the position of the maximum change amplitude in the change trend does not exceed the share of the resource that can be carried in the current scheduling task, scheduling the plurality of resources with the highest matching degree in the scheduling candidate information to the demand point.

7. The method for resource scheduling based on map data fusion according to claim 3, wherein, The method further comprises: The resource scheduling system obtains a feature descriptor of the demand point; The resource scheduling system performs similarity calculation on the feature descriptor and the fusion feature information of the target database; In a case where the similarity calculation value is greater than or equal to a target value, the resource scheduling system determines that the demand point matches the fusion feature information.

8. The resource scheduling method based on map data fusion of claim 1, wherein, The method further comprises: Before the resource scheduling system initiates the scheduling instruction to the demand point according to the fused map data, the resource to be scheduled is in an unscheduled state.

9. The method for resource scheduling based on map data fusion according to claim 1, wherein, The method further comprises: In a case where the share of the other resource being scheduled in the resource scheduling system is less than a first value, the resource scheduling system continues to generate the scheduling instruction.

10. A resource scheduling system, characterized by, The resource scheduling system for implementing the method of any one of claims 1 to 9 comprises: An acquisition unit configured to acquire resource scheduling information, wherein the resource scheduling information comprises a plurality of resources, and the plurality of resources are a plurality of resources with the highest priority determined based on map data fusion; the plurality of resources are different from resources that have been successfully scheduled; A detection unit configured to obtain scheduling condition data of each of the resources in different geographic regions in a historical time period; A determination unit configured to, in a case where a scheduling instruction is detected in the current map, sequentially select different shares of the resources, and sequentially obtain a plurality of scheduling data according to the scheduling condition data corresponding to the plurality of resources with the highest matching degree in the resource scheduling information and the scheduling condition data corresponding to all the resources; wherein the share of the plurality of resources with the highest matching degree in the resource scheduling information is associated with the share of the selected resource. The calculation unit is used to obtain the share of the selected resource corresponding to the position with the largest change in the change trend based on the change trend between multiple scheduling data and the share of the selected resource; The scheduling unit is used to select the share of the resource corresponding to the position with the largest change in the change trend and schedule the resource to the demand point.

Images